1. Technical Field
The invention relates to the visual exploration of consistent patterns in customer purchase behavior across time. More particularly, the invention relates to a purchase sequence browser.
2. Description of the Prior Art
A Model of the Retail Behavior of Customers
Customer purchase behavior may be characterized as a mixture of projections of time-elapsed latent purchase intentions. A customer purchases a particular product at a certain time in a certain store with a certain intention, e.g. weekly grocery, back-to-school, etc. An intention is latent, i.e. it is not obvious or announced, although it may be deduced from the context of the products purchased. Each visit by a customer to the store may reflect one or more (mixture of intentions. Each intention may involve purchase of one or more products. For a multi-product intention, it is possible that the customer may not purchase all the products associated with that intention either at the same store or in the same visit. The transaction data only reflects a subset or a projection of a latent intention for several reasons, for example, maybe the customer already has some of the other products associated with the intention, or he received them as a gift, or he purchased them at a different store, etc. Finally, an intention may be spread across time. For example, certain intentions, such as kitchen remodeling or setting up a home office, may take several weeks and multiple visits to different stores.
A Model of Retail Transaction Data
Retail transaction data may be characterized as a time-stamped sequence of market baskets. The key characteristics of such a transaction data are:                Noisy—both intentional and impulsive purchases;        Incomplete—only projections of intentions present;        Overlapping—mixture of intentions in the same visit;        Indirect—purchase drivers or customer intentions are latent;        Unstructured—customers have different length time histories; and        Time-component—patterns in the data elapse along time.        
These characteristics pose challenges in discovering consistent and significant patterns of purchase behavior from transaction data that may be used for making precise, timely, and profitable decisions by retailers.